Adaptive Data Processing, Modeling, and Quantification Methods for Analyzing Cardiac Fibrillation

用于分析心颤的自适应数据处理、建模和量化方法

基本信息

  • 批准号:
    RGPIN-2020-04933
  • 负责人:
  • 金额:
    $ 2.04万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Discovery Grants Program - Individual
  • 财政年份:
    2020
  • 资助国家:
    加拿大
  • 起止时间:
    2020-01-01 至 2021-12-31
  • 项目状态:
    已结题

项目摘要

Heart is a vital organ that beats (i.e. expands and contracts) nonstop to maintain blood circulation to keep us alive. The rhythmic contractions and expansion of the heart transports nutrients and oxygen via blood to all parts of the body to sustain life. When this rhythmic functioning of the heart gets disturbed because of various pathophysiological reasons, arrhythmic contractions result in compromising the normal functioning of the heart. Depending on the origin of these arrhythmic contractions, it may lead to lethal conditions. The most lethal of the arrhythmias is Ventricular Fibrillation (VF) which originates from the lower chambers of the heart (i.e. ventricles). VF can lead to sudden cardiac death (SCD) if no medical attention is provided within minutes of onset. About 300,000 SCDs are reported every year in North America (45,000 in Canada) most of which are VF related. Atrial fibrillation (AF) originating from atria, although not as lethal as VF, can seriously impact quality of life and increases the risk of stroke. Despite research efforts over many decades, there is still a significant knowledge gap in understanding the mechanistic basis of cardiac fibrillation which is preventing effective means to reduce the mortality rates associated with cardiac fibrillation (especially for VF). This strongly motivates the need for developing new engineering methods in understanding mechanisms behind these arrhythmias and translating them to realizable practical solutions to reduce the mortality associated with the arrhythmias. Major bottle necks in decoding the mechanisms behind lethal VF is that SCD occurs within minutes and that in most cases [especially in out-of-the-hospital cardiac arrests (OHCA)] the only immediately available information on the electrical state of the heart is through surface electrocardiograms. In addressing the above knowledge gap, the proposed research program will develop new ways of analyzing and extracting information from multi-channel electrograms and electrocardiograms during arrhythmia and build computer simulation models to decipher the mechanistic insights of cardiac fibrillation. Specifically, the research, in collaboration with Toronto General and St. Michael's Hospitals, will develop advanced data processing and modeling techniques to characterize and regionally locate the sources that initiate and sustain cardiac arrhythmias. The informative clues on these fibrillatory sources will be appropriately translated into electrograms and multi-channel electrocardiogram signal morphologies. These discriminative signal morphologies along with the evolution of the arrhythmia over time will then be used to develop intelligent ablation and defibrillation strategies. The mechanistic knowledge gained through the proposed research program and the developed analysis strategies will significantly augment long-term focused (in-hospital) medical strategies for arrhythmias as well as improve survival rates in OHCA.
心脏是一个至关重要的器官,它不停地跳动(即扩张和收缩),以维持血液循环,维持我们的生命。心脏有节奏的收缩和扩张通过血液将营养和氧气输送到身体的各个部位,以维持生命。当心脏的这种节律功能由于各种病理生理原因而受到干扰时,心律失常的收缩会损害心脏的正常功能。根据这些不规律的收缩的来源,它可能导致致命的情况。最致命的心律失常是心室颤动(VF),它起源于心脏的下腔(即心室)。如果在发作后几分钟内没有医疗护理,室性心动过速可导致心源性猝死。北美每年约有30万例scd报告(加拿大45000例),其中大多数与VF有关。心房颤动(AF)起源于心房,虽然不像室性心动过速那样致命,但可严重影响生活质量并增加中风的风险。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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Umapathy, Karthikeyan其他文献

College Students' Conceptions of Learning of and Approaches to Learning Computer Science
  • DOI:
    10.1177/0735633119872659
  • 发表时间:
    2020-06-01
  • 期刊:
  • 影响因子:
    4.8
  • 作者:
    Umapathy, Karthikeyan;Ritzhaupt, Albert D.;Xu, Zhen
  • 通讯作者:
    Xu, Zhen
Phase Mapping of Cardiac Fibrillation
  • DOI:
    10.1161/circep.110.853804
  • 发表时间:
    2010-02-01
  • 期刊:
  • 影响因子:
    8.4
  • 作者:
    Umapathy, Karthikeyan;Nair, Krishnakumar;Nanthakumar, Kumaraswamy
  • 通讯作者:
    Nanthakumar, Kumaraswamy
Classification of lung pathologies in neonates using dual-tree complex wavelet transform.
  • DOI:
    10.1186/s12938-023-01184-x
  • 发表时间:
    2023-12-04
  • 期刊:
  • 影响因子:
    3.9
  • 作者:
    Aujla, Sagarjit;Mohamed, Adel;Tan, Ryan;Magtibay, Karl;Tan, Randy;Gao, Lei;Khan, Naimul;Umapathy, Karthikeyan
  • 通讯作者:
    Umapathy, Karthikeyan
Intramural Activation During Early Human Ventricular Fibrillation
  • DOI:
    10.1161/circep.110.961037
  • 发表时间:
    2011-10-01
  • 期刊:
  • 影响因子:
    8.4
  • 作者:
    Nair, Krishnakumar;Umapathy, Karthikeyan;Nanthakumar, Kumaraswamy
  • 通讯作者:
    Nanthakumar, Kumaraswamy
Aborted sudden death from sustained ventricular fibrillation
  • DOI:
    10.1016/j.hrthm.2008.04.005
  • 发表时间:
    2008-08-01
  • 期刊:
  • 影响因子:
    5.5
  • 作者:
    Nair, Krishnakumar;Umapathy, Karthikeyan;Nanthakumar, Kumaraswamy
  • 通讯作者:
    Nanthakumar, Kumaraswamy

Umapathy, Karthikeyan的其他文献

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{{ truncateString('Umapathy, Karthikeyan', 18)}}的其他基金

Adaptive Data Processing, Modeling, and Quantification Methods for Analyzing Cardiac Fibrillation
用于分析心颤的自适应数据处理、建模和量化方法
  • 批准号:
    RGPIN-2020-04933
  • 财政年份:
    2022
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Discovery Grants Program - Individual
Adaptive Data Processing, Modeling, and Quantification Methods for Analyzing Cardiac Fibrillation
用于分析心颤的自适应数据处理、建模和量化方法
  • 批准号:
    RGPIN-2020-04933
  • 财政年份:
    2021
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Discovery Grants Program - Individual
Adaptive Signal Modeling and Feature Extraction Methods for Analyzing Cardiac Fibrillation
用于分析心颤的自适应信号建模和特征提取方法
  • 批准号:
    RGPIN-2015-06644
  • 财政年份:
    2019
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Discovery Grants Program - Individual
Adaptive Signal Modeling and Feature Extraction Methods for Analyzing Cardiac Fibrillation
用于分析心颤的自适应信号建模和特征提取方法
  • 批准号:
    RGPIN-2015-06644
  • 财政年份:
    2018
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Discovery Grants Program - Individual
Adaptive Signal Modeling and Feature Extraction Methods for Analyzing Cardiac Fibrillation
用于分析心颤的自适应信号建模和特征提取方法
  • 批准号:
    RGPIN-2015-06644
  • 财政年份:
    2017
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Discovery Grants Program - Individual
Adaptive Signal Modeling and Feature Extraction Methods for Analyzing Cardiac Fibrillation
用于分析心颤的自适应信号建模和特征提取方法
  • 批准号:
    RGPIN-2015-06644
  • 财政年份:
    2016
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Discovery Grants Program - Individual
Adaptive Signal Modeling and Feature Extraction Methods for Analyzing Cardiac Fibrillation
用于分析心颤的自适应信号建模和特征提取方法
  • 批准号:
    RGPIN-2015-06644
  • 财政年份:
    2015
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Discovery Grants Program - Individual
Signal and image processing methods for studying human ventricular fibrillation
研究人体心室颤动的信号和图像处理方法
  • 批准号:
    386738-2010
  • 财政年份:
    2014
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Discovery Grants Program - Individual
Signal and image processing methods for studying human ventricular fibrillation
研究人体心室颤动的信号和图像处理方法
  • 批准号:
    386738-2010
  • 财政年份:
    2013
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Discovery Grants Program - Individual
Signal and image processing methods for studying human ventricular fibrillation
研究人体心室颤动的信号和图像处理方法
  • 批准号:
    386738-2010
  • 财政年份:
    2012
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Discovery Grants Program - Individual

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    2022
  • 资助金额:
    $ 2.04万
  • 项目类别:
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